Updating risk prediction tools a case study in prostate cancer
Of these 25 444 were blood; 41 315 breast; 32 626 bowel, 12 808 gastro-oesophageal; 32 187 lung; 4811 oral; 6635 ovarian; 7119 pancreatic; 35 256 prostate; 23 091 renal tract; 6949 uterine cancers.The lung cancer algorithm had the best performance with an R of 64.2%; D statistic of 2.74; receiver operating characteristic curve statistic of 0.91 in women.We determined an initial entry date to the cohort for each patient, which was the latest of the following dates: 25th birthday, date of registration with the practice plus 1 year, date on which the practice computer system was installed plus 1 year, and the beginning of the study period (1 January 1998).
For each type of cancer, we excluded patients with a history of the relevant cancer any time prior to the study start date.
The sensitivity for the top 10% of women at highest risk of lung cancer was 67%.
Performance of the algorithms in men was very similar to that for women.
We randomly allocated three-quarters of practices to the derivation data set and the remaining quarter to a validation data set.
We identified an open cohort of patients aged 25–84 years drawn from patients registered with practices between 1 January 1998 and 30 September 2013.
Search for updating risk prediction tools a case study in prostate cancer:
Conclusions We have developed and validated a prediction models to quantify absolute risk of 11 common cancers.